DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 4, 7-11, 14 and 17-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lee et al. (2021/0397813).
Regarding claim 1, Lee teaches a method of processing fingerprint image data (para [0235] he method shown in FIG. 16 may perform primary fingerprint authentication), the method comprising: receiving, by a control system (DRV; Fig 1A), fingerprint image data (para [0089] Fig 1A; para [0089] The driver DRV may detect a fingerprint shape of a user by using the received electrical signals. para [0093] The fingerprint detector DRV_FP may detect or recognize a fingerprint based on a sensing signal SS transferred from the sensor pixels SPXL; para [0232] The method shown in FIG. 16 may sense a fingerprint (S1610).); determining, by the control system, whether authentication based on the fingerprint image data is successful (para [0093] For example, the fingerprint detector DRV_FP may convert the sensing signal SS into a fingerprint image (e.g., image data), and perform fingerprint authentication based on the fingerprint image.; Fig 16, step S1630); based on a determination that authentication based on the fingerprint image data is unsuccessful (Fig 16: step S1630), obtaining, by the control system, an enhanced version of the fingerprint image data (Fig 16: step S1650; para [0237] When the primary fingerprint authentication is not successfully performed (or when the primary fingerprint authentication fails), the method shown in FIG. 16 may perform image processing on the fingerprint image (S1650).); and providing, by the control system, the enhanced version of the fingerprint image data for authentication (Fig 16; para [0239] Subsequently, the method shown in FIG. 16 may perform secondary fingerprint authentication based on the high-quality fingerprint image (S1660) ).
Regarding claim 4, Lee teaches the method of claim 1, wherein the enhanced version of the fingerprint image data is obtained from an image enhancement machine learning model (para [0070] As shown in FIG. 8, the learning unit MLU may convert the enrolled image IMAGE_E (e.g., the first fingerprint image IMAGE1) included in the template TEMP into a pseudo image IMAGE_P by using an image conversion model. The image conversion model may be an algorithm for converting an image having a high quality (e.g., a large signal-to-noise ratio) into an image having a low quality (e.g., a small signal-to-noise ratio).para [0174] In some exemplary embodiments, the learning unit MLU may set or update an image conversion model through learning of fingerprint images IMAGE_L for learning. Para [0231]).
Regarding claim 7, Lee teaches the method of claim 1, wherein determining, by the control system, whether authentication based on the fingerprint image data is successful further comprises: determining, by the control system, whether fingerprint features extracted from the fingerprint image data match fingerprint features extracted from fingerprint image data obtained during a fingerprint enrollment process (para [0164] In an exemplary embodiment, the determining unit DTU may extract first feature points from the second fingerprint image IMAGE2, and compare the first feature points with second feature points of each of the fingerprint images (e.g., the enrolled image IMAGE_E) in the template TEMP. Each of the first feature points and the second feature points may be a point at which a ridge of the fingerprint is ended and/or a point at which the ridge branches off. The determining unit DTU may calculate an accordance rate (e.g., a matching score) between the first feature points and the second feature points. The determining unit DTU may authenticate the second fingerprint image IMAGE2 based on the accordance rate; para [0235] Subsequently, the method shown in FIG. 16 may perform primary fingerprint authentication based on the fingerprint image (e.g., a second fingerprint image IMAGE2) (S1630). For example, the method shown in FIG. 16 may perform the primary fingerprint authentication by comparing the fingerprint image with a template TEMP (e.g., an enrolled image IMAGE_E, a first pseudo image IMAGE_P1, a second pseudo image IMAGE_P2, and a third pseudo image IMAGE_P3).).
Regarding claim 8, Lee teaches the method of claim 1, further comprising: based on a determination that authentication based on the enhanced version of the fingerprint image data is successful, storing, by the control system, the enhanced version of the fingerprint image data for future authentication attempts (para [0168] When the fingerprint authentication is successfully performed with the second fingerprint image IMAGE2, the third fingerprint image IMAGE3 along with the second fingerprint image IMAGE2 may be stored in the storage unit STU to be used in the learning unit MLU which will be described later. Para [0175] The fingerprint images IMAGE_L for learning may include high-quality fingerprint images and low-quality fingerprint images of each of a plurality of objects (e.g., a plurality of fingerprints of users).).
Regarding claim 9, Lee teaches the method of claim 1, wherein providing, by the control system, the enhanced version of the fingerprint image data for authentication further comprises: providing, by the control system, fingerprint features extracted from the enhanced version of the fingerprint image data to be matched against fingerprint features extracted based on fingerprint image data obtained during a fingerprint enrollment process (para [0161] When the quality of the first fingerprint image IMAGE1 is lower than the reference quality, the determining unit DTU may re-acquire a first fingerprint image IMAGE1 through the sensor PS and the image processing unit IPU, and again determine a quality of the re-acquired first fingerprint image IMAGE1. When the quality of the re-acquired first fingerprint image IMAGE1 is higher than or equal to the reference quality (e.g., quality value), the determining unit DUT may generate the template TEMP based on the re-acquired first fingerprint image IMAGE1, e.g., by adding the re-acquired first fingerprint image IMAGE1 to the template TEMP. para [0162] The determining unit DTU may generate the template TEMP including the plurality of fingerprint images IMAGE_E1, IMAGE_E2, . . . , and IMAGE_Ek shown in FIG. 7 by determining qualities of a plurality of first fingerprint images IMAGE1, which are acquired in the fingerprint enrollment period or acquired for each position at the finger. Para [0164] In an exemplary embodiment, the determining unit DTU may extract first feature points from the second fingerprint image IMAGE2, and compare the first feature points with second feature points of each of the fingerprint images (e.g., the enrolled image IMAGE_E) in the template TEMP. Each of the first feature points and the second feature points may be a point at which a ridge of the fingerprint is ended and/or a point at which the ridge branches off. The determining unit DTU may calculate an accordance rate (e.g., a matching score) between the first feature points and the second feature points. The determining unit DTU may authenticate the second fingerprint image IMAGE2 based on the accordance rate. For example, the determining unit DTU may authenticate that a user of the second fingerprint image IMAGE2 is an enrolled user.).
Regarding claim 10, Lee teaches the method of claim 9, wherein the fingerprint image data obtained during the fingerprint enrollment process is enhanced based on an image enhancement machine learning model prior to the fingerprint features being extracted ( para [0016] The learning unit may be configured to set the first image conversion model through learning of fingerprint images for learning, which are provided from outside of the fingerprint authentication device, and wherein the fingerprint images for learning may include high-quality fingerprint images of which signal-to-noise ratios are within a reference quality range and low-quality fingerprint images of which signal-to-noise ratios are out of the reference quality range with respect to a plurality of objects. Para [0100] the fingerprint detector DRV_FP does not only add the low-quality sensed fingerprint image to the template, but may convert the enrolled fingerprint image into the pseudo image by using the image conversion model such that the converted pseudo image (e.g., a new fingerprint image for authentication) reflects or includes various conditions (e.g., a state of skin and authentication environment). Thus, a fingerprint may be accurately recognized in additional states of skin of a user and/or in an unexpected authentication environment, which are not considered in fingerprint enrollment. Para [0161] When the quality of the first fingerprint image IMAGE1 is lower than the reference quality, the determining unit DTU may re-acquire a first fingerprint image IMAGE1 through the sensor PS and the image processing unit IPU, and again determine a quality of the re-acquired first fingerprint image IMAGE1. When the quality of the re-acquired first fingerprint image IMAGE1 is higher than or equal to the reference quality (e.g., quality value), the determining unit DUT may generate the template TEMP based on the re-acquired first fingerprint image IMAGE1, e.g., by adding the re-acquired first fingerprint image IMAGE1 to the template TEMP.).
Regarding claim 11, Lee teaches an apparatus, comprising: one or more fingerprint sensors (Fig 1A; para [0083] In an exemplary embodiment, the display device DD may further include a plurality of sensor pixels SPXL disposed in the fingerprint sensing area FSA.); and a control system (DRV; Fig 1A) configured to: receive fingerprint image data (Fig 1A; para [0089] The driver DRV may detect a fingerprint shape of a user by using the received electrical signals. para [0093] The fingerprint detector DRV_FP may detect or recognize a fingerprint based on a sensing signal SS transferred from the sensor pixels SPXL; para [0232] The method shown in FIG. 16 may sense a fingerprint (S1610).); determine whether authentication based on the fingerprint image data is successful (para [0093] For example, the fingerprint detector DRV_FP may convert the sensing signal SS into a fingerprint image (e.g., image data), and perform fingerprint authentication based on the fingerprint image.; Fig 16, step S1630); based on a determination that authentication based on the fingerprint image data is unsuccessful (Fig 16: step S1630), obtain an enhanced version of the fingerprint image data (Fig 16: step S1650; para [0237] When the primary fingerprint authentication is not successfully performed (or when the primary fingerprint authentication fails), the method shown in FIG. 16 may perform image processing on the fingerprint image (S1650).); and provide the enhanced version of the fingerprint image data for authentication (Fig 16; para [0239] Subsequently, the method shown in FIG. 16 may perform secondary fingerprint authentication based on the high-quality fingerprint image (S1660) ).
Regarding claims 14, 17-19, which are similar in scope to claims 4, 7-9 respectively, therefore claims 14, 17-19 are rejected same as claim 4, 7-9 as explained above.
Regarding claim 20, Lee teaches One or more non-transitory computer-readable media having instructions for performing a method stored thereon (para [0069]), the method comprising: receiving fingerprint image data (Fig 1A; para [0089] The driver DRV may detect a fingerprint shape of a user by using the received electrical signals. para [0093] The fingerprint detector DRV_FP may detect or recognize a fingerprint based on a sensing signal SS transferred from the sensor pixels SPXL; para [0232] The method shown in FIG. 16 may sense a fingerprint (S1610).); determining whether authentication based on the fingerprint image data is successful (para [0093] For example, the fingerprint detector DRV_FP may convert the sensing signal SS into a fingerprint image (e.g., image data), and perform fingerprint authentication based on the fingerprint image.; Fig 16, step S1630); based on a determination that authentication based on the fingerprint image data is unsuccessful (Fig 16: step S1630), obtaining an enhanced version of the fingerprint image data (Fig 16: step S1650; para [0237] When the primary fingerprint authentication is not successfully performed (or when the primary fingerprint authentication fails), the method shown in FIG. 16 may perform image processing on the fingerprint image (S1650).); and providing the enhanced version of the fingerprint image data for authentication (Fig 16; para [0239] Subsequently, the method shown in FIG. 16 may perform secondary fingerprint authentication based on the high-quality fingerprint image (S1660) ).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 5, 6, 15 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (2021/0397813) in view of Riazi et al. (2023/0075233).
Regarding claim 5, Lee teaches the method as explained for claim 4 above.
Lee fails to teach, wherein the image enhancement machine learning model comprises a generative-adversarial network (GAN) that includes at least one generator that transforms the fingerprint image data to the enhanced version of the fingerprint image data; as claimed.
Riazi teaches a method of processing fingerprint image data, the method comprising: an image enhancement machine learning model comprises a generative-adversarial network (GAN) that includes at least one generator that transforms the fingerprint image data to the enhanced version of the fingerprint image data (para [0025] The GAN 110 provides deep learning models that can estimate the distribution of a given dataset, such as a dataset of real fingerprints found in database 105, for example. In the GAN 110, two different neural networks are trained in parallel. The first neural network, the generative network, tries to generate synthetic fingerprint samples from the given data distribution of real fingerprints 105. Para [0049]).
It would have been obvious to one of ordinary skill in the art before the filing date of present application to have modified the method of Lee with the teachings of Riazi, because this will provide fingerprints may then be generated at scale (e.g., to provide large quantities of fingerprints) to enable properly verifying the performance of fingerprint based systems, training machine learning systems to accurately perform tasks (e.g., fingerprint detection), and/or the like (Riazi: para [0017]).
Regarding claim 6, Lee teaches the method as explained for claim 5 above.
Lee fails to teach, wherein the GAN includes at least one discriminator that evaluates the enhanced version of the fingerprint image data, and wherein the at least one generator is refined based at least in part on the evaluation; as claimed.
Riazi teaches the method, wherein the GAN includes at least one discriminator that evaluates the enhanced version of the fingerprint image data, and wherein the at least one generator is refined based at least in part on the evaluation (para [0032] During training, the generative network (labeled generator 110A) tries to generate synthetic fingerprint samples 266A from the given data distribution of real fingerprints 205B. These synthetic (labeled fake 266A) fingerprint samples are provided to the discriminator network 110B, so the discriminator 110 can learn to distinguish the synthetic fingerprint samples (which are generated by the generative network) from the real fingerprint data samples at database 205B. During the training phase of the GAN, the feedback 266B from the discriminator 110B network to the generator 110A is used to enhance the quality of the samples produced by the generator 110A. In this way, the GAN 110 when trained can provide a model that estimates the probability distribution of real fingerprints.).
It would have been obvious to one of ordinary skill in the art before the filing date of present application to have modified the method of Lee with the teachings of Riazi, because this will provide fingerprints may then be generated at scale (e.g., to provide large quantities of fingerprints) to enable properly verifying the performance of fingerprint based systems, training machine learning systems to accurately perform tasks (e.g., fingerprint detection), and/or the like (Riazi: para [0017]).
Regarding claims 15 and 16, which are similar in scope to claims 5 and 6 respectively, therefore claims 15 and 16 are rejected same as claim 5 and 6 as explained above.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claim comparison table
Claims of Application 19/362,876
Claims of U.S. Patent# 12,450,940
2. The method of claim 1, further comprising: prior to obtaining the enhanced version of the fingerprint image data, determining, by the control system, that the fingerprint image data was captured by a given fingerprint sensor of a device, wherein the given fingerprint sensor produces lower quality fingerprint image data relative to one or more other fingerprint sensors of the device.
1. A method of processing fingerprint image data, the method comprising: receiving, by a control system, fingerprint image data; determining, by the control system, whether the fingerprint image data was captured by a first fingerprint sensor of a device or a second fingerprint sensor of the device, wherein the second fingerprint sensor produces lower quality fingerprint image data relative to the first fingerprint sensor; based on a determination that the fingerprint image data was captured by the second fingerprint sensor of the device, obtaining, by the control system, an enhanced version of the fingerprint image data; and providing, by the control system, the enhanced version of the fingerprint image data for authentication.
3. The method of claim 2, wherein the device is a foldable display device having a first panel comprising the one or more other fingerprint sensors and a second panel comprising the given fingerprint sensor.
2. The method of claim 1, wherein the device is a foldable display device having a sub panel comprising the first fingerprint sensor and a main panel comprising the second fingerprint sensor.
12. The apparatus of claim 11, wherein, prior to obtaining the enhanced version of the fingerprint image data, the control system is further configured to: determine that the fingerprint image data was captured by a given fingerprint sensor of the apparatus, wherein the given fingerprint sensor produces lower quality fingerprint image data relative to one or more other fingerprint sensors of the apparatus.
13. An apparatus, comprising: a first fingerprint sensor; a second fingerprint sensor; and a control system configured to: receive fingerprint image data; determine whether the fingerprint image data was captured by the first fingerprint sensor or the second fingerprint sensor, wherein the second fingerprint sensor produces lower quality fingerprint image data relative to the first fingerprint sensor; based on a determination that the fingerprint image data was captured by the second fingerprint sensor, obtain an enhanced version of the fingerprint image data; and provide the enhanced version of the fingerprint image data for authentication.
13. The apparatus of claim 12, wherein the apparatus is a foldable display device having a first panel comprising the one or more other fingerprint sensors and a second panel comprising the given fingerprint sensor.
14. The apparatus of claim 13, wherein the apparatus is a foldable display device having a sub panel comprising the first fingerprint sensor and a main panel comprising the second fingerprint sensor.
Claims 2, 3, 12 and 13 are rejected on the ground of nonstatutory double patenting as being unpatentable over Lee et al. (2021/0397813) in view of claims 1, 2, 13 and 14 of U.S. Patent No. 12,450,940. Regarding claim 2, prior art of record Lee teaches the method of claim 1 as explained above. Lee fails to teach, prior to obtaining the enhanced version of the fingerprint image data, determining, by the control system, that the fingerprint image data was captured by a given fingerprint sensor of a device, wherein the given fingerprint sensor produces lower quality fingerprint image data relative to one or more other fingerprint sensors of the device; as claimed. However, Claim 1 of U.S. Patent# 12,450,940 teaches the method wherein prior to obtaining the enhanced version of the fingerprint image data, determining, by the control system, that the fingerprint image data was captured by a given fingerprint sensor (second fingerprint sensor of claim 1 of U.S. Patent #12,450,940) of a device, wherein the given fingerprint sensor produces lower quality fingerprint image data relative to one or more other fingerprint sensors (first fingerprint sensor of claim 1 of U.S. Patent #12,450,940) of the device (see claim comparison table above). It would have been obvious to one of ordinary skill in the art before the filing date of present application to have modified the method of Lee with the teachings of U.S. Patent# 12,450,940, because this will yield predictable results of improving fingerprint sensing. Regarding claim 3, prior art of record Lee teaches the method of claim 1 as explained above. Lee fails to teach, wherein the device is a foldable display device having a first panel comprising the one or more other fingerprint sensors and a second panel comprising the given fingerprint sensor; as claimed. However, Claim 2 of U.S. Patent# 12,450,940 teaches the method wherein the device is a foldable display device having a first panel (sub panel of claim 2 of U.S. Patent #12,450,940) comprising the one or more other fingerprint sensors and a second panel (main panel of claim 2 of U.S. Patent #12,450,940) comprising the given fingerprint sensor (see claim comparison table above). It would have been obvious to one of ordinary skill in the art before the filing date of present application to have modified the method of Lee with the teachings of U.S. Patent# 12,450,940, because this will yield predictable results of improving fingerprint sensing. Regarding claim 12, prior art of record Lee teaches the apparatus of claim 11 as explained above. Lee fails to teach, prior to obtaining the enhanced version of the fingerprint image data, determining, by the control system, that the fingerprint image data was captured by a given fingerprint sensor of a device, wherein the given fingerprint sensor produces lower quality fingerprint image data relative to one or more other fingerprint sensors of the device; as claimed. However, Claim 13 of U.S. Patent# 12,450,940 teaches the apparatus wherein prior to obtaining the enhanced version of the fingerprint image data, determining, by the control system, that the fingerprint image data was captured by a given fingerprint sensor (second fingerprint sensor of claim 13 of U.S. Patent #12,450,940) of a device, wherein the given fingerprint sensor produces lower quality fingerprint image data relative to one or more other fingerprint sensors (first fingerprint sensor of claim 13 of U.S. Patent #12,450,940) of the device (see claim comparison table above). It would have been obvious to one of ordinary skill in the art before the filing date of present application to have modified the device of Lee with the teachings of U.S. Patent# 12,450,940, because this will yield predictable results of improving fingerprint sensing. Regarding claim 13, prior art of record Lee teaches the apparatus of claim 11 as explained above. Lee fails to teach, wherein the apparatus is a foldable display device having a first panel comprising the one or more other fingerprint sensors and a second panel comprising the given fingerprint sensor; as claimed. However, Claim 14 of U.S. Patent# 12,450,940 teaches the apparatus wherein the apparatus is a foldable display device having a first panel (sub panel of claim 14 of U.S. Patent #12,450,940) comprising the one or more other fingerprint sensors and a second panel (main panel of claim 14 of U.S. Patent #12,450,940) comprising the given fingerprint sensor (see claim comparison table above). It would have been obvious to one of ordinary skill in the art before the filing date of present application to have modified the device of Lee with the teachings of U.S. Patent# 12,450,940, because this will yield predictable results of improving fingerprint sensing.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Li et al. (2020/0012311) teaches A joystick includes a body, a feature identifier and a processor. The body has a lateral portion and a bottom portion connected with each other. The bottom portion is adapted to be pressed, and the lateral portion whereon an identification element is disposed is adapted to support the bottom portion. The feature identifier is disposed under the body and adapted to acquire an identification result about the body.
Di et al. (2024/0005695) teaches A fingerprint recognition method and an electronic device. The electronic device includes a touch screen and a fingerprint sensor. The electronic device displays a first interface and captures first fingerprint information that is input by a user at the first interface. The electronic device determines whether the touch screen is in a first state, and the first state is used to indicate that the touch screen is in a screen protector state. If the touch screen is in the first state, the electronic device restores the first fingerprint information by using a first preset artificial intelligence (AI) restoration model to obtain second fingerprint information. The electronic device determines that the second fingerprint information matches preset fingerprint information and displays a second interface. Quality of a fingerprint image in the second fingerprint information is higher than quality of a fingerprint image in the first fingerprint information.
Buchan et al. (2021/0073511) teaches An apparatus may include an ultrasonic sensor system, a low-frequency vibration source and a control system. The ultrasonic sensor system may include an ultrasonic receiver and an ultrasonic transmitter configured for transmitting ultrasonic waves in a first frequency range (e.g., 1 MHz to 30 MHz). The low-frequency vibration source may be configured for generating low-frequency vibrations in a second frequency range (e.g., the range of 5 Hz to 2000 Hz). The control system may be configured for synchronizing the generation of the first low-frequency vibrations and the transmission of the first ultrasonic waves.
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/PREMAL R PATEL/Primary Examiner, Art Unit 2624
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